# A Commute in Data: The comma2k19 Dataset

arXiv: https://arxiv.org/abs/1812.05752 · HTML: https://mlsanity.com/paper/1812.05752/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2018.

## Built on by (most cited first, 30 of 33)

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- [Towards Driving-Oriented Metric for Lane Detection Models](https://mlsanity.com/paper/2203.16851/) (2022)
- [End-to-end Uncertainty-based Mitigation of Adversarial Attacks to Automated Lane Centering](https://mlsanity.com/paper/2103.00345/) (2021)
- [Level 2 Autonomous Driving on a Single Device: Diving into the Devils of Openpilot](https://mlsanity.com/paper/2206.08176/) (2022)
- [On Robustness of Lane Detection Models to Physical-World Adversarial Attacks in Autonomous Driving](https://mlsanity.com/paper/2107.02488/) (2021)
- [FisheyePP4AV: A privacy-preserving method for autonomous vehicles on fisheye camera images](https://mlsanity.com/paper/2309.03799/) (2023)
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- [First qualitative observations on deep learning vision model YOLO and DETR for automated driving in Austria](https://mlsanity.com/paper/2312.12314/) (2023)
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- [A global dataset of continuous urban dashcam driving](https://mlsanity.com/paper/2604.01044/) (2026)
- [OmniScientist: An Omni-Modal Omni-Discipline AI Scientist](https://mlsanity.com/paper/2608.13558/) (2026)
- [DriveMotion: A Large-Scale Multi-Source Benchmark for Driver Motion Sequence Modeling and Forecasting](https://mlsanity.com/paper/2609.08117/) (2026)
- [Fusing Pseudo Labels with Weak Supervision for Dynamic Traffic Scenarios](https://mlsanity.com/paper/2308.15960/) (2023)
- [Spatiotemporal-Aware Bit-Flip Injection on DNN-based Advanced Driver Assistance Systems (extended version)](https://mlsanity.com/paper/2604.03753/) (2026)
- [ParkingScenes: A Structured Dataset for End-to-End Autonomous Parking in Simulation Scenes](https://mlsanity.com/paper/2604.22835/) (2026)
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- [Creating Impactful Autonomous Driving Datasets: A Strategic Guide from Research Gap to Benchmark](https://mlsanity.com/paper/2607.00710/) (2026)

## Abstract (arXiv)

comma.ai presents comma2k19, a dataset of over 33 hours of commute in California's 280 highway. This means 2019 segments, 1 minute long each, on a 20km section of highway driving between California's San Jose and San Francisco. The dataset was collected using comma EONs that have sensors similar to those of any modern smartphone including a road-facing camera, phone GPS, thermometers and a 9-axis IMU. Additionally, the EON captures raw GNSS measurements and all CAN data sent by the car with a comma grey panda. Laika, an open-source GNSS processing library, is also introduced here. Laika produces 40% more accurate positions than the GNSS module used to collect the raw data. This dataset includes pose (position + orientation) estimates in a global reference frame of the recording camera. These poses were computed with a tightly coupled INS/GNSS/Vision optimizer that relies on data processed by Laika. comma2k19 is ideal for development and validation of tightly coupled GNSS algorithms and mapping algorithms that work with commodity sensors.
